In this work, we aim to address these questions by leveraging Self-Supervised Learning (SSL) for material property prediction. Unlike supervised learning which uses labels for supervision, SSL makes use of the large unlabeled data for supervision to learn robust and generalizable representations that can be used for various tasks. Recently, SSL frameworks such as SimCLR[1]} , Barlow Twins [2]} , BYOL[3]} , SwAV [4]} , momentum contrastive learning [5]} , SimSiam [6]} , Albert [7]} , and self-supervised dialogue learning [8]} have been successfully applied to computer vision and natural language processing tasks. The success of these SSL methods has inspired many works in molecular ML, leading to the development of highly accurate frameworks such as MolCLR [9]} , dual view molecule pre-training [10]} , 3D Infomax [11]} , and numerous other popular works[12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]}.
<FIGURE>